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⚡ Rust Image Search

Source: examples/rust/image‑search

This guide demonstrates building an image-based similarity search application using the Rust SDK. It covers:

  • Initializing ahnlich-db and ahnlich-ai stores
  • Ingesting image files as raw bytes into an AI Store
  • Running similarity queries based on image embeddings
  • Using metadata (e.g. tags) for filtering results
  • Returning both vector similarity scores and original image references

🔧 What you’ll learn​

  1. Setting up an AI Store via Rust client.
  2. Feeding images (byte streams) into the store.
  3. Querying the store with a new image file to retrieve visually similar results.
  4. Applying metadata filters (e.g. tag == "nature").
  5. Understanding handling of raw input retention for display.

💡 Highlighted snippet​

let mut ai_client = AIClient::new("127.0.0.1:1370".to_string()).await?;
let tracing_id = None;

// create a store for images
ai_client.create_store(CreateStoreAI {
    store: "image_store".to_string(),
    index_model: AIModel::AllMiniLML6V2,
    query_model: AIModel::AllMiniLML6V2,
    predicates: Some(HashSet::from(["tag".to_string()])),
    store_original: Some(true),
    error_if_exists: Some(true),
}, tracing_id.clone()).await?;

// ingest image bytes with metadata
ai_client.set(SetAI {
    store: "image_store".to_string(),
    inputs: vec![
      (StoreInput::RawBytes(img_bytes), HashMap::from([("tag".to_string(), "nature")]))
    ],
    preprocess_action: Some(PreprocessAction::NoPreprocessing),
}, tracing_id.clone()).await?;

➕ Try it yourself​

  • Clone the example repository
  • Launch ahnlich-db and ahnlich-ai locally via Docker or binaries
  • Modify the image folder path and metadata tags
  • Build and run the Rust example app to visualize results